Profit & Loss:
When it comes to producing meaningful TCA, what are the big data challenges
facing market participants?

Petra Wikstrom:
Over the last five years we’ve seen a constant uptick in the electronification
of FX, but the number of venues offering FX liquidity has increased far beyond
that, which means that similar volumes are now offered across more venues.

This naturally means that when we use electronic venues for
the assessment of pricing information that we need to tap into more of these
venues. Then we have to interpret that data to seek a representative view of
the market.

FX TCA requires a pragmatic engineering approach for drawing
the right conclusions. We don’t have access to the full data pool of liquidity
or prices across the market and so we have to make educated estimates from what’s
available. For example, for the ECNs this means looking at a combination of the
primary and also secondary venues.

From this we can estimate benchmarks, but then we have to
assess which is the right type of benchmarks depending on the individual
investor’s underlying rationale of the execution, and how to appropriately interpret
the results.

So on the post-trade side, the biggest challenge is that we
don’t have a full universe of data for benchmark calculation. But creating
meaningful post-trade TCA is not an impossible task by any means.

P&L: And on the
pre-trade side?

PW: On the
pre-trade side, we want to be able to make an educated estimate of the
performance for our future execution as a way of understanding market impact for
different sized orders and execution strategies given the current liquidity at
hand.

So we have to measure those liquidity conditions and the
pool of liquidity that is utilised to try and get an as representative as
possible view of the overall market. Now in the liquid G10 currencies, where
between 80% and 90% of the market is traded electronically, there is enough
data to give us a fairly good representation of the market.

The same approach is more limited in the forwards, swaps and
NDF market, even though the amount of electronic NDF trading has increased in
the past couple of years. But if we can get some information and data around
these products we see investors are usually very interested in it, especially
as combined with profound trading experience.

P&L: There seems
to be some skepticism from market participants regarding bank provided
post-trade TCA. This seems to centre around concerns that banks are really
justifying the price they gave rather than providing a thorough assessment of
that execution. What do you make of this?

PW: When it comes
to post-trade TCA and post-trade evaluation of performance, I think that it’s
absolutely key for any provider to be highly transparent around what universe
of data is included in the report and how the benchmark is calculated. And as
the market becomes increasingly transparent there are ways to essentially get a
second opinion to confirm the benchmark calculation by another provider.

The advantage that sell side TCA can provide is the
intellectual capital and quantitative expertise to help improve the client’s
execution. So the sell side has the intellectual capital across numerous
business centres – whether it’s electronic market making, automated execution,
voice trading, etc, – that it can leverage to help investors gain insight about
their execution process.

What this does is helps investors find areas where there are
outliers or patterns that show room for improvement in their execution, which
in turn creates a positive feedback loop to the pre-trade solution.

P&L: Using algos
is often pushed forward as the solution for improving execution now. Why is
this?

PW: There are a
couple of things going on simultaneously here. Although it depends on the
investor, there is generally a push for more automation of process and audit
trail of execution where there can be.

Then, depending on the liquidity of the market, where we
have seen significant structural changes over the past few years, some clients
might choose to split an order up over time to limit market impact. But then
another reason for using algos could be that a particular execution strategy
will allow a firm to get easier access to a larger pool of liquidity, but then
naturally adapt to market conditions as the execution is live.

Overall, I think that more firms are using algos right now
as a means to limit market impact and save costs, specifically for large order
size. But it’s important to remember that when we talk about large orders that
everything is relative. For example, what is “large” in EUR/USD would be
different if you were trading the Polish zloty or the Korean won.

The maturity and existence of these more automated
strategies is really in the liquid G10 space and some deliverable EM, but that
doesn’t mean that firms can’t still work an order over a period of time in a
more manual fashion to try and limit market impact for other pairs.

P&L: When it
comes to using algos, isn’t there a problem of being able to judge the
performance of different algos against each other?

PW: There are a
few points to note here. If a firm executes a large order in a certain fashion,
then it’s hard to directly compare that to another strategy executed at a
different point in time because the liquidity conditions could have been very
different. That’s one aspect to think about, there could, for example, have
been an economic event happening, which could have impacted liquidity
conditions, and price volatility, and made the executions different.

Another aspect to think about is that in order to accurately
judge the performance of any execution strategy, firms need a large sample of executions.
Someone who has done five trades with a strategy could say that it works
excellently, it could be a drift phenomenon or it could actually be excellent, but
without enough statistics, it’s hard to rigorously evaluate.

Yet another thing to consider is whether the algos being
compared are being used for the same objective, because if they aren’t then it
could, in addition to the relative performance, be down to the trade-off between
limiting market impact and taking price risk. Overall, to address the relative
performance of strategies, one needs to have the same objective and use for the
same-sized orders and over the same liquidity conditions.

So a passive investor might choose to work an order over a
long period of time to limit market impact, and in doing so, be willing to take
on some price risk. In contrast, someone else might choose to do a more
aggressive execution and offload the risk more quickly – even though this will
probably have a higher cost – because they don’t want to take on this price
risk. So it’s important to remember that it’s not necessarily a one size fits
all solution.